{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "data_dir = 'toydata'\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=True)\n",
    "              for x in ['train', 'val']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 224, 224])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs, classes = next(iter(dataloaders['val']))\n",
    "inputs.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SingleToy', 'Scenes', 'Scenes', 'NoToy']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[class_names[x] for x in classes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 2,  1,  1,  0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset ImageFolder\n",
       "    Number of datapoints: 24\n",
       "    Root Location: toydata/val\n",
       "    Transforms (if any): Compose(\n",
       "                             Resize(size=256, interpolation=PIL.Image.BILINEAR)\n",
       "                             CenterCrop(size=(224, 224))\n",
       "                             ToTensor()\n",
       "                             Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "                         )\n",
       "    Target Transforms (if any): None"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_datasets['val']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NoToy', 'Scenes', 'SingleToy']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_datasets['train'].classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def imshow(inp, title=None):\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=1):\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "                model.train() \n",
    "            else:\n",
    "                model.eval()  \n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "                optimizer.zero_grad()\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "        print()\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model_vgg = models.vgg11(pretrained=True)\n",
    "model_vgg = model_vgg.to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer_vgg = optim.SGD(model_vgg.parameters(), lr=0.001, momentum=0.9)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_vgg, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_ftrs = model_vgg.classifier[6].in_features\n",
    "features = list(model_vgg.classifier.children())[:-1]\n",
    "features.extend([nn.Linear(num_ftrs, 3)])\n",
    "model_vgg.classifier = nn.Sequential(*features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/0\n",
      "train Loss: 0.9720 Acc: 0.5128\n",
      "val Loss: 0.8111 Acc: 0.5833\n",
      "\n",
      "Best val Acc: 0.583333\n"
     ]
    }
   ],
   "source": [
    "model_vgg = train_model(model_vgg, criterion, optimizer_vgg, exp_lr_scheduler,\n",
    "                       num_epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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51rou7U6FsUmfrDfKZz79GI7n4QUeQhl8T2FXLLlcGs9TELg4KA7uvZdGNE87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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ff8d677b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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adZHKoT8YELkKYRwKHSP8nCLLSaVHqd6aXnPyrLSeOKpprtnb7/Doo0/whU89RP34mxgN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\nB4VwAX66VQTtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ff4550908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HJ1LNvoSJLrn0tHfRcDWCSoOO1jSnp+YIKdDc1sEThw9z8cWX4BoGJ04dp7ejg0QsSjaf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\nSlzyqs9x7Wtfgtn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AJ8s4gISE6wd47K57kY8eRpYkHM8jmwgi93YTCAeQdBU9oON7Pg3D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\nPcoP71F+eI/6D2JQkvVAaE3iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ff44ce438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TazexLufbzz/FBx4+TFyLSdK9WfxoXxBvYkEkkGVDQgikaQ1rC5SC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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ff4498470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5zs2onolSSJKfmuS3ofPov/fj/OCh33LBZX+HmZ+gmM9jlBzGJ0M0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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ff44612e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wuW0gmWlw+1A0OtaTZfKBLGsrp6mvqcHqVlmfSaFUq0QSaRIRiYnJBSoGFwdv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\nKDO8QZnhDcoMb1BmeIMywxuUGd6g/gebC3UjmOj78wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ff43c8c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_vgg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
